Integrating AI-generated content tools in higher education: a comparative analysis of interdisciplinary learning outcomes

This study examines the integration of artificial intelligence-generated content (AIGC) tools across disciplines in higher education settings. Using a mixed-methods approach, we analyzed implementation patterns and learning outcomes across humanities, STEM, and social sciences programs at multiple institutions. Findings revealed a 37% increase in interdisciplinary project outcomes (measured by collaborative problem-solving scores, cross-domain knowledge integration ratings, and peer evaluation metrics) when AIGC tools were strategically implemented, with significant variations in effectiveness based on implementation approach. While these technologies demonstrated substantial value in breaking down disciplinary silos and accommodating diverse learning preferences, challenges emerged regarding algorithmic bias, digital equity, and maintenance of discipline-specific skills. This research contributes to educational theory by proposing a revised framework for AI-human collaboration in knowledge production. We conclude with policy recommendations for governance frameworks that balance innovation with academic integrity, emphasizing faculty co-design approaches and the establishment of cross-disciplinary communities of practice.